Joint Space-Time Optimum Filter (JSTOF) Using Cholesky and Eigenvalue Decompositions

ABSTRACT

A filter for reducing co-channel interference within a communications receiver may include a multi-channel, space-time filter circuit that filters n signal parts that have been split from a communications signal by jointly estimating space-time filter weights and multi-channel impulse responses (CIRs) based upon Cholesky and eigenvalue decompositions. The filter may further include a multi-channel, matched filter circuit that receives multi-channel signals from the multi-channel, space-time filter circuit and has a filter response that is provided by a channel impulse response estimation from the space-time filter circuit

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.60/708,277, filed Aug. 15, 2005, which is hereby incorporated herein inits entirety by reference.

FIELD OF THE INVENTION

The present invention relates to wireless communications systems, suchas cellular communications systems, and, more particularly, to filteringreceived wireless signals to reduce unwanted interference.

BACKGROUND

Interference canceling matched filters (ICMF) and joint demodulation(JDM) has been investigated to meet requirements for a Downlink AdvancedReceiver Performance (DARP) that is standardized by the third generationmobile communications system and the Third Generation PartnershipProject (3GPP). Some of these proposals are set forth in the followingarticles and documents

-   -   1. Liang et al., A Two-Stage Hybrid Approach for CCI/ISI        Reduction with Space-Time Processing, IEEE Communication Letter        Vol. 1, No. 6, November 1997.    -   2. Pipon et al., Multichannel Receives Performance Comparison In        the Presence of ISI and CCI, 1997 13th Intl. Conf. on Digital        Signal Processing, July 1997,    -   3. Spagnolini, Adaptive Rank-One Receiver for GSM/DCS Systems,        IEEE Trans. on Vehicular Technology, Vol. 51, No. 5, September        2002.    -   4. Feasibility Study on Single Antenna Interference Cancellation        (SAIC) for GSM Networks, 3GPP TR 45.903 Version 6.0.1, Release        6, European Telecommunications Standards Institute, 2004.    -   5. Radio Transmission and Reception (Release 6), 3GPP TS 45.005        Version 6.8.0; European Telecommunications Standards Institute,        2005.    -   6. Stoica et al., Maximum Likelihood Parameter and Rank        Estimation in Reduced-Rank Multivariate Linear Regressions, IEEE        Trans. On Signal Processing, Vol. 44, No. 12, December 1996.    -   7. Kristensson et al., Blind Subspace Identification of a BPSK        Communication Channel, Proc. 30^(th) Asilomar Conf. On Signals,        Systems and Computers, 1996.    -   8. Golub et al., Matrix Computations, 3^(rd) Edition, 1996.    -   9. Trefethen et al., Numerical Linear Algebra, 1997.    -   10. Press et al., Numerical Recipes in C, 2^(nd) Edition, 1992.

Current Global System for Mobile communications (GSM) cellular systemshave to address the co-channel interference (CCI) on the mobile station(MS) side, as well as address the DARP requirements. Some single channelstructures and pre-filters have been used to aid in canceling theinterference and provide some channel impulse response (CIR) estimation.Moreover, some systems have used maximization of thesignal-to-interference to design jointly a single channel space-timefilter and the CIR estimation for a single channel. Other systems haveused a constrained minimization of the mean-square error to design asingle channel space filter. Other systems have used a single channelspace filter that is designed by a rank-one approximation of the MLchannel estimation. The target applications for these systems have beena base station where a physical antenna array including a plurality ofantennas is available.

BRIEF DESCRIPTION OF THE DRAWINGS

Various objects, features and advantages will become apparent from thefollowing detailed description, when considered in light of theaccompanying drawings, in which:

FIG. 1 is a block diagram of a Joint Space-Time Optimum Filter basedDownlink Advanced Receiver Performance (DARP) capable receiver inaccordance with an exemplary embodiment;

FIGS. 2 is a more detailed block diagram of the Joint Space-Time OptimumFilter and Multi-Channel Matched Filters shown in FIG. 1 in accordancewith an exemplary embodiment;

FIG. 2A is a block diagram of a method in accordance with an exemplaryembodiment;

FIG. 3 is a graph showing the Joint Space-Time Optimum Filter based DARPcapable receiver performance for various DARP test cases;

FIG. 4 is a graph showing the Joint Space-Time Optimum Filter receiverperformance in accordance with an exemplary embodiment with additivewhite Gaussian noise (AWGN), compared with and without an auto-switchingstrategy;

FIG. 5 is a graph showing the Joint Space-Time Optimum Filter receiverperformance in accordance with an exemplary embodiment with DTS-5,compared with and without auto-switching;

FIG. 6 is a graph comparing the performance of single with multipleViterbi equalizers in accordance with an exemplary embodiment, using8-bit SD limiter in the simulation;

FIG. 7 is a graph showing the performance of Joint Space-Time OptimumFilter Receiver and a modified test case in accordance with an exemplaryembodiment;

FIG. 8 is a schematic block diagram of an exemplary model wirelesscommunication device that can be used in accordance with an exemplaryembodiment; and

FIG. 9 is a table comparing the three approaches for performing Choleskydecomposition, QR decomposition, and singular value decomposition (SVD)computations in accordance with the present disclosure.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

Several non-limiting embodiments will now be described more fullyhereinafter with reference to the accompanying drawings, in whichpreferred exemplary embodiments are shown. These embodiments may,however, be embodied in many different forms and should not be construedas limited to the embodiments set forth herein. Rather, theseembodiments are provided so that this disclosure will be thorough andcomplete, and will fully convey the scope to those skilled in the art.Like numbers refer to like elements throughout, and prime notation isused to indicate similar elements in alternative embodiments.

In accordance with one embodiment, Co-Channel Interference (CCI) on amobile station (MS) side in a current Global System for Mobile (GSM)communications system is addressed, as well as the compliant requirementof a Downlink Advanced Receiver Performance (DARP) standard by the ThirdGeneration Partnership Project (3GPP).

Generally speaking, the present disclosure relates to a filter forreducing co-channel interference within a communications receiver. Moreparticularly, the filter may include a multi-channel, space-time filtercircuit that filters signal parts that have been split from acommunications signal by jointly estimating space-time filter weightsand multi-channel impulse responses (CIRs) based upon a Choleskydecomposition. A multi-channel matched filter circuit receivesmulti-channel signals from the multi-channel, space-time filter circuitand has a filter response that is provided by a channel impulse responseestimation from the space-time filter circuit. A standard filter can beoperative when an interference level is below a pre-determined thresholdand can be formed as a matched filter and cross-correlation circuit andswitch mechanism for switching the signal parts into the matched filterand cross-correlation circuit.

In one aspect, the multi-channel, space-time filter circuit includes aplurality of multiplier and delay circuits that each receive n signalparts. The multiplier and delay circuits are operative based onspace-time filter weights. Each multiplier and delay circuit comprisestwo multiplier circuits and a delay circuit. Each multiplier and delaycircuit is operative at one symbol delay. A joint optimal filter weightsand channel estimator is operatively connected to the multi-channel,space-time filter circuit and receives training sequence (TS) symbolsand timing uncertainty data and generates space-time filter weights forthe multi-channel, space-time filter circuit. A summer circuit sums datafrom the multiplier and delay circuits for each channel. An equalizercircuit is operative with the multi-channel, matched filter circuit.

The illustrated embodiment in FIG. 1 provides a multi-channel pre-filterthat is operable for canceling the interference and providing channelimpulse response (CIR) estimation adaptively and optimally. Thepre-filter can use two major components in one non-limiting example: (1)a multiple-input-multiple-output (MIMO) based Joint Space-Time OptimumFilter (JSTOF); and (2) a multiple-input-single-output (MISO) basedmulti-channel matched filter. In a typical mobile station using a singleavailable antenna, a virtual antenna array can be configured internallyby the combination of over sampling and the separation of real andimaginary parts that receive samples.

In one non-limiting embodiment, a signal from the virtual antenna arrayis fed to the JSTOF, where the optimum weights for the MIMO-basedinterference canceling filter are estimated. At the same time, themulti-channel CIRs for the desired signal are jointly estimated. Theoutput of the JSTOF allows the interference to be filtered and fed to aMISO-based multi-channel matched filter. The filter response of thematched filter is provided by the CIR estimation from the JSTOF.

The output of the multi-channel matched filter passes to a Viterbiequalizer which removes the inter-symbol interference (ISI) and providessoft decisions for further processing. A single channel responserequired by the equalizer can be formed by a combination of theconvolved CIRs from the JSTOF. This pre-filter can also automaticallyswitch to the conventional or standard filter in the conventionalreceiver in any AWGN dominant cases and switch back to the JSTOF-basedreceiver in any interference dominant cases. This auto-switchingcapability reduces the loss in AWGN dominant cases.

An example of the pre-filter or interference canceling filter for theJSTOF-based and DARP-capable receiver is shown at 10 in FIG. 1, in whichthe oversampling ratio is 2 and the number of the virtual antennas is 4(M=4), as also indicated by X₁(k) through X₄(k). Throughout thisdescription the pre-filter 10 can be referred to as the interferencecanceling filter or JSTOF filter, and acts as a pre-filter in a DARPcompliant receiver. A receiver incorporating this filter 10 could bedescribed as a JSTOF receiver as is shown by the dashed line at 11 inFIG. 1.

FIG. 1 shows examples of the various circuit blocks used for the filter10. An input signal is received into a derotation circuit 12. Thederotated output signal is split, with a portion passing into a filter14 of a conventional receiver that includes a 2:1 switch 16, and anoutput into a matched filter 18 and a cross-correlation circuit 20 thatreceives shortened training sequence (TS) symbols. The 2:1 switch 16 isoperable to allow switching between the filter 14 and the JSTOF-basedand DARP-capable pre-filter 10.

The other portion of the output signal from the derotation circuit 12 issplit into even samples and odd samples as part of the virtual antenna24 and split again into real and imaginary signals to form therespective X₁(k) through X₄(k) input signals into a JSTOF circuit 30,also referred to as a multi-channel, space-time filter circuit. Theoutput signals from the JSTOF circuit are passed into a multi-channelmatched filter circuit 32, and its output signal is passed into aresealing circuit 34 and then into a multiplexer circuit 36 as data(d₁). The multiplexer circuit 36 also receives a channel (c₁) response.When the conventional filter 14 is connected, the multiplexer 36receives the data (d₂) and channel (c₂) response from the matched filtercircuit 18 and cross-correlation circuit 20. Signals are passed into aViterbi equalizer 38 as a soft decision output.

Further details of the JSTOF and the multi-channel matched filters areshown in FIG. 2, where the number of time-delayed samples used in theJSTOF circuit is 2 (N=2) The various inputs X₁(k) through X₄(k) arereceived into the JSTOF, which is shown in greater detail. The JSTOFcircuit 30 includes channelized multipliers, also termed mixers 40, 42,delay units 44 and summers 46, which input into multi-channel matchedfilters 48 for each of the four illustrated channels, and signals fromthe matched filters are passed into a summer 50. A joint optimal filterweights and channel estimator circuit 52 receives TS symbols and timinguncertainty signals to produce the weights (W_(OPT)) used for the mixers40, 42.

It thus is possible as described to integrate a pre-filter function intoa conventional GSM receiver by adding a pre-filter branch parallel to aconventional matched filter as shown in FIG. 1. The conventionalsoftware/hardware Viterbi equalizer 38 can be used without change. Inone non-limiting example, an integrated DARP-capable receiver has beentested against DARP test cases by simulations, which indicates that thereceiver provides 1.6 dB to 6.9 dB margin over a specified performancein terms of the frame error rate (FER) for one of the AMR speechchannels.

FIG. 2 a is a flow-chart illustrating a high-level method associatedwith the described system in which the various steps are shown asnon-limiting examples. The various steps begin with the 100 seriesreference numerals. The incoming communications signal is derotated(Block 100) and passed into the virtual antenna. The communicationssignal is split into even and odd samples (Block 102), and each even andodd sample is then split into real and imaginary signal parts (Block104). The communications signals from the virtual antenna are passedinto the JSTOF circuit, where the communications signals are multipliedand delayed (Block 106) and then summed (Block 108), all as part of afirst multiple-in, multiple-out (MIMO) Joint Space Time Optimum Filter(JSTOF). After summing, summed signals are passed into themulti-channel, multiple-input single-output (MISO) matched filtercircuit (Block 110) and then summed (Block 112) and passed as a singleout signal into the Virterbi equalizer (Block 114) in which a softdecision is made (Block 116).

In operation, the derotation circuit 12 is operable with GMSK modulatedsignals and the frequency offset that is part of that signalingprotocol. Before any derotation, the signal constellation is dynamic andafter derotation the signal constellation becomes static, i.e., anysymbols are typically concentrated on 0° and 180°, with symbols rotatedto those two points. Thus, the GMSK signal can be treated as a typicalbinary phase shift keying (BPSK) signal. The derotation at the front endis used for even and odd samplings, which is useful because of theover-sampling rate. For example, in a conventional receiver, this istypically at the rate of 1, i.e., one sample per symbol.

The virtual antenna 24 can increase the sampling rate to two samples persymbol in a serial manner coming from the baseband filter to form twoseparate channels of even and odd. Before this process, the odd/evensamples were serially interleaved. These signals are then further splitinto the real and imaginary signal parts to form the four independentchannels of the virtual antenna. It should be noted that in someembodiments other numbers of virtual antennas/channels may be used(e.g., one or more), as will be appreciated by those skilled in the art.

As best shown in FIG. 2, these signals are then passed into themultiplier 40, 42 and unit delay circuits 44, e.g., a one symbol delay,and thus the signal is processed with a multiply and delay, followed bya multiply operation as evident by the two multipliers 40, 42 and onedelay circuit 44. This operation is followed by a summation in summer 46as illustrated. This portion of the system is operable as amulti-channel, two-dimensional filter. One dimension occurs because ofthe delay in time and another dimension is introduced from the virtualantenna, i.e., a spatial dimension as described and thus the twodimensions form a space-time filter.

It is evident each incoming signal is used in conjunction with otherchannels, and multipliers receive weights from the Joint Optimal FilterWeights and Channel Estimator 52. The weights coming from the JointOptimal Filter Weight and Channel Estimator 52 are passed into themultipliers.

The weights are also an 8×4 dimensional matrix in one non-limitingexample, i.e., 32 weights. As to the training sequence symbols inputinto the Joint Optimal Filter Weights and Channel Estimator 52, thereare typically in some non-limiting examples about 26 known symbols andit is known which training sequence a packet contains. A +/−3 or sevenpositions search in a non-limiting example can be used to find thetiming. The impulse response of the multi-channel matched filter(h_(opt)) can be used such that the system matches the channel responseand makes the signal stronger after the matched filter.

As shown in FIG. 1, resealing can occur as a hardware or softwareconvenience, although it is not required. This resealing circuit 34allows greater operation for a 4-bit or 5-bit input as a non-limitingexample to the Viterbi equalizer 38. The dynamic range of the signal canbe readjusted such that the signal can be sent into a 4-bit or 5-bitcircuit.

As noted before, the multiplexer 36 can take the signals d₂ and c₂ forthe data and channel response from the conventional filter receiver 14or the signals d₁ and c₁ for the data and channel response from theJSTOF receiver 10 to allow a switch-over between the two. The JSTOFreceiver will introduce some loss if there is no interference, i.e.,just pure white noise. In this case the conventional receiver 14 can beused and will work adequately. So, the circuits can switch back to theconventional filter without loss introduced by the JSTOF receiver andits circuits. The switching is based on the estimation of the SINR_(OUT)minus SINR_(INP). If the quantity is below a threshold, the systemdetermines there is little interference and the interference cancelingof the JSTOF receiver is not required. Thus, the filter of theconventional receiver 14 is used by switching the 2:1 switch 16.

The circuit is operable in beam forming systems and other systems. Thistype of system also allows the signal-to-noise ratio to be improved andthe bit error rate (BER) to be improved. This could impact top levelprotocols and phone calls and other communications matters for use withthese circuits.

The multi-channel structure of the JSTOF-based filter 10 is used in oneembodiment, and the MIMO-based JSTOF circuit 30 provides a space-timefilter weight and channel estimations that are different from prior artsolutions. This circuit provides the ability to combat the interferenceefficiently for both synchronous and asynchronous interferences andyield high performance. Some simulations have shown that none of thesolutions in some prior art techniques provide the required performanceagainst the DARP test cases.

This MISO-based multi-channel matched filter circuit 32 is a featurethat improves the overall error rate performance and reduces thecomplexity of the equalizer by avoiding multi-channel Viterbiequalizers. The built-in automatic switching between JSTOF-based andconventional receivers reduce the loss in AWGN cases.

Suitable receiver structures can be used in order to meet the DARPrequirements. An Interference Canceling Matched Filter (ICMF) can use anexample of the virtual antenna as described and beamforming to combatthe interference. The circuit is sensitive to the estimation errors ofthe Channel Impulse Response (CIR) of the desired signal. A JointDemodulation (JD) showed good performance for the various test cases. Inaddition to the difficulty in combating the asynchronous interferers,there may be heavy computational complexity involved in finding the CIRof an interferer.

In one embodiment, the virtual antenna 24 is operable with adaptivespace-time filtering, allowing the Joint Spatial-Temporal Optimum Filter(JSTOF) circuit 30 to be used. One difference from the ICMF is that thespatial-temporal filter weights used to suppress the interference andthe CIR estimation of the desired signal are jointly estimated andoptimized in the JSTOF while the two are separately estimated in anICMF. The JSTOF circuit 30 can be a Multiple-Input-Multiple-Output(MIMO) circuit that takes advantage of the rank deficiency nature of thedesired CIR matrix in the space-time setup. Simulations have shown asatisfactory performance for the various DARP test cases. Computationalload is deemed acceptable given that fixed-point Cholesky factorizationand EVD/SVD are feasible.

This method has some simplicity and low computational complexity. It isalso robust because the system makes few assumptions about the source ofthe interference. In addition, the system can continue to use theexisting equalizer structure, as the solution is integrated as apre-processing step on the input data. This would allow the system touse the HW equalizer accelerators if available.

In order to support the evaluation of this technique, the system levelBlock Error Rate (BLER) simulator was extended to support all of theinterferer models/scenarios being used by the 3GPP DARP Specification.

There now follows a description of the simulation performance for DARPtest cases using the JSTOF circuit. It should be understood thatspace-time processing for joint interference reduction and channelestimation has been used in a base station, where an array of M antennasis available. Assuming that the equivalent channel response for thesingle desired user can be modeled as an L-tap Finite Impulse Response(FIR) filter, a snapshot sample of the received baseband signal can beexpressed as $\begin{matrix}{{{x(k)} = {{{\sum\limits_{l = 0}^{L - 1}{{c(l)}s_{k - l}}} + {v(k)}} = {{{Hs}(k)} + {v(k)}}}},} & (1)\end{matrix}$where x(k) is an M×1 vector representing the output from the antennas, His an M×L matrix containing the channel response for the antenna array,s(k) is an L×1 vector for the corresponding symbols transmitted, andv(k) is an M×1 vector including the AWGN and the interference. Thespace-time extension for formula (1) can be obtained by stacking Ntime-delayed versions of x(k) into a taller MN×1 vector x(k) as follows:x (k)=[x ^(T)(k),x ^(T)(k−1), . . . ,x ^(T)(k−N+1)]^(T) = H s (k)+ v(k),  (2)where H an MN×(L+N−1) matrix is the block Toeplitz version of H ands(k)=[S_(k), s_(k−1), . . . , s_(k−L−N+2)]T. The samples that correspondto the training sequence can be collected,X=[ x (k), x (k+1), . . . , x (k+p−1)]= HS + V ,  (3)where p=P−L−N+2, P is the number of symbols of the training sequence, Xis an MN×p matrix, and S=[ s(k), s(k+1), . . . , s(k+p−1)] is an(L+N−1)×p convolution matrix of the training symbols. The jointoptimization is to find a non-trivial MN×1 weight vector w for aspace-time filter and a non-trivial (L+N−1)×1 channel estimation vectorh after the filter such that the output interference residual of thefilter is minimized, i.e., to solve the following optimization problem:$\begin{matrix}{\min\limits_{w,h}{{{{w^{T}\overset{\_}{X}} - {h^{T}\overset{\_}{S}}}}^{2}.}} & (4)\end{matrix}$It can be found that the optimal weight is:w _(opt) =R _(x) ⁻¹ R _(xs) h _(opt),  (5)and the optimal channel estimation h_(opt) is the eigenvectorcorresponding to the minimum eigenvalue of the matrix R_(s)−R_(xs)^(H)R_(x) ⁻¹R_(xs), where $\begin{matrix}{{R_{x} = {{\overset{\_}{X}}^{*}{\overset{\_}{X}}^{T}}},\left( {{MN} \times {MN}} \right)} & (6) \\{{R_{s} = {{\overset{\_}{S}}^{*}{\overset{\_}{S}}^{T}}},{\left( {\left( {L + N - 1} \right) \times \left( {L + N - 1} \right)} \right)\quad{and}}} & (7) \\{{R_{xs} = {{\overset{\_}{X}}^{*}{\overset{\_}{S}}^{T}}},{\left( {({MN}) \times \left( {L + N - 1} \right)} \right).}} & (8)\end{matrix}$Given that the noise plus interference component V in the space-timemodel of equation (3) is no longer white but approximately Gaussiandistributed with unknown covariance matrix R_(v), the optimal estimationfor the channel H is the maximum-likelihood (ML) estimation, which is aminimization of the following quantity: $\begin{matrix}{{l\left( {\overset{\_}{H},R_{v}} \right)} = {{\log{R_{v}}} + {{{\overset{\_}{X} - \overset{\_}{HS}}}_{R_{v}^{- 1}}^{2}.}}} & (9)\end{matrix}$In this non-limiting space-time model, the number of the independentchannels is always less than or equal to M and H is usually rankdeficient, i.e., rank( H)=r<min(MN, L+N−1). The rank deficient MLproblem can be used for the rank-1 approximation of the space-timefilter.

The JSTOF circuit in one embodiment can use a different approach to findthe joint optimum solutions for the filter weight and the channelestimation. It is possible to find the ML estimation of H. Theestimation can be decomposed asĤ=Ĥ_(s) Ĥ _(T) ^(H),  (10)where Ĥ_(s) (MN×M) is the estimation of the space matrix of H andĤ_(T)((L+N−1)×M) is the estimation of the time matrix of H. They can beobtained by:{circumflex over (H)}_(T) =R _(s) ^(−1/2) V _(DM), and  (11)Ĥ _(s) =R _(xs) Ĥ _(T),  (12)where R_(s)=R_(s) ^(1/2)R_(s) ^(H/2) is the Cholesky factorization andV_(DM) consists of the M eigenvectors corresponding to the top Meigenvalues of the matrix D,D=R _(s) ^(H/2) R _(xs) ^(H) R _(x) ⁻¹ R _(xs) R _(s) ^(1/2).  (13)In a next step, the optimal weight for the space-time filter can beobtained byw _(opt) =R _(x) ⁻¹ R _(xs) Ĥ _(T), (MN×M)  (14)and the optimal channel estimation ish _(opt) =w _(opt) ^(T) Ĥ(M×(L+N−1))  (15)

It is then possible to apply the optimal space-time filter in equation(14) to the samples from the antenna array 24. Clearly the outputs ofthe filter 30 still have M channels, and it is a MIMO system. Theoptimal channel estimation in equation (15) can be used for themulti-channel matched filters 32. The outputs of the matched filter arethen combined (summed up) and rescaled in the resealing circuit 34 tothe modified desired level. The final output is a single-channel samplestream and can be fed into the Viterbi equalizer 38. Note also that thenumber of channel taps after the JSTOF has been changed to L+N−1comparing to L of the modeled channel taps before the JSTOF.

It was observed by simulations that the JSTOF receiver incurred morethat 1 dB loss in the pure AWGN cases compared to the conventionalreceiver using the conventional filter. To reduce the loss, a strategyof automatic switching between the JSTOF and conventional receivers wasdeveloped. The switching is based on the measurement of the differenceof the input and output SINR's of the JSTOF. When the difference isbelow a predefined threshold the JSTOF receiver is turned off and theconventional receiver is turned on. The input SINR can be easilycomputed once the estimation of H is done in equation (10):$\begin{matrix}{{{SINR}_{inp} = {\frac{{{\hat{H}\overset{\_}{S}}}^{2}}{{{\overset{\_}{X} - {\hat{H}\overset{\_}{S}}}}^{2}} = \frac{{tr}\left( {{\hat{H}}^{*}R_{s}{\hat{H}}^{T}} \right)}{{tr}\left( {R_{x} + {{\hat{H}}^{*}R_{s}{\hat{H}}^{T}} - {2{Re}\left\{ {R_{xs}{\hat{H}}^{T}} \right\}}} \right)}}},} & (16)\end{matrix}$and the output SINR can be computed from equations (14) and (15):$\begin{matrix}{{SINR}_{out} = {\frac{{{h_{opt}\overset{\_}{S}}}^{2}}{{{{w_{opt}^{T}\overset{\_}{X}} - {h_{opt}\overset{\_}{S}}}}^{2}} = {\frac{{tr}\left( {h_{opt}^{*}R_{s}h_{opt}^{T}} \right)}{{tr}\left( {{w_{opt}^{H}R_{x}w} + {h_{opt}^{*}R_{s}h_{opt}^{T}} - {2{Re}\left\{ {w_{opt}^{H}R_{xs}h_{opt}^{T}} \right\}}} \right)}.}}} & (17)\end{matrix}$On the mobile side, a virtual antenna array can be set up by thecombination of oversampling and the separation of the real and imaginaryparts as shown in FIG. 1.

In accordance with various embodiments, the joint optimum MIMOspace-time filter and channel estimation set forth in equations (14) and(15) enhances interference suppression performance. The MISOmulti-channel matched filters 32, which are based on the channelestimation in equation (15), improve the error rate performance whilereducing the complexity of the Viterbi equalizer 38. A strategy ofautomatic switching between JSTOF and conventional receivers reduces theloss in pure AWGN cases.

The JSTOF defined by equations (6)-(17) can be implemented in differentways in terms of numerical stability and computational complexity. Themajor differences are the way in which the inverse of theautocorrelation matrix R_(x) is calculated and the way in which thechannel Ĥ is estimated with reduced rank.

One such implementation is a Cholesky decomposition-based matrixinversion of R_(x) and the eigenvalue decomposition of matrix D inequation (13). Specifically, since R_(x) is symmetric positive definite,the Cholesky decomposition exists:R =L _(x) L _(x)   (18)D can be rewritten asD=D ₁ D ₁ ^(T)  (19)whereD ₁ =L _(s) ^(−T) R _(xs) ^(T) L _(x) ^(−T)  (20)

It should be noted that the inverse is actually performed with thesquare-root of R_(x), and the explicit computation of the inverse may beavoided by the back-substitution. Also, D is numerically stable becauseof its structure of mutual cancellations. This was verified bysimulations that showed the condition number of D is seldom greater than300. This implies the eigenvalue decomposition on D would not requireunduly sophisticated algorithms for typical applications, as will beappreciated by those skilled in the art. In fact, this approach maypotentially have the least computational complexity of the approachesoutlined herein.

One potential numerical concern is the Cholesky decomposition on R_(x),as its condition number may potentially be relatively high, and itspositive definite property may be offset to some degree by round-offerrors. Simulations showed, however, that the condition number of R_(x)is less than 10⁷ even in some extreme scenarios such as very high andvery low carrier-to-interference (C/I) ratios.

In accordance with an alternate embodiment, the QR decomposition in thesample domain may be used to avoid the direct calculation of the inverseof R_(x). Since the X ^(T) in equation (3) has full column rank, it hasthe unique QR decompositionX ^(T) =QR,  (21)where Q is a p×MN matrix with orthogonal columns and R is a full rankMN×MN upper triangular matrix. It can be shown thatR _(x) ^(−T) =R ^(−T) R ^(−T),  (22)and the D in equation (13) can be written in the form of equation (19)with the D₁ re-defined byD ₁ =L _(s) ^(−T) SQ.  (23)

The reduced rank channel estimation may be performed with the eigenvaluedecomposition on D as in the previous approach, and the optimum filterweight matrix of (14) can be reduced asw _(opt) =R ⁻¹ D _(l) ^(T) V _(DM)  (24)

This approach is basically an equivalent version of Choleskydecomposition in the sample domain since one can show that R=L_(x) ^(T).It has improved numerical stability at the expense of the QRdecomposition's greater complexity (requiring approximately twice asmany operations for a matrix of given size) and larger sample matrix(having approximately 3 times as many rows in an example case where M=4,N=2 and L=5).

The two approaches described above still require the computation of thetriangular matrix inverse, although this may be done byback-substitutions. Turning now to yet another alternate approach, i.e.,the singular value decomposition (SVD) approach, the matrix inversionmay be avoided and the numerical stability may be further improved insome applications. This approach starts with the SVD on the samplematrix in equation (3):X ^(T) =U _(x)Σ_(x) V _(x) ^(T),  (25)where U_(x) is a p×MN matrix with orthogonal columns, V_(x) is an MN×MNorthogonal matrix and Σ_(x) is an MN×MN diagonal matrix, Σ_(x)=diag(σ₁,. . . , σ_(MN)), with the singular values on its diagonal. It can beshown thatR _(x) ⁻¹ =V _(x)Σ_(x) ⁻² V _(x) ^(T).  (26)The D in equation (13) still has the form of equation (19) with D₁defined by:D ₁ =L _(s) ^(−T) SU _(x).  (27)The channel estimation may be obtained by the SVD on D₁ and the filterweight matrix may be written asw _(opt) =V _(x)Σ_(x) ⁻¹ D ₁ ^(T) V _(DM),  (28)where V_(DM) contains the top M right singular vectors of D₁. The SVD inthis approach may require more computations than the Cholesky and QRdecompositions used in the previous two approaches.

As a comparison of the three approaches outlined above (i.e., Cholesky,QR, and SVD), the table in FIG. 9 lists the computations step by stepfor an example where M=4, N=2 and L=5. To find the best timing of theburst, the JSTOF searches a number of timing hypotheses and the onecorresponding to the minimum output residual is chosen as the besttiming. The output residual is defined by:e=∥w _(opt) ^(T) X−h _(opt) ^(T) S∥ ²,   (29)The search process basically repeats the operations listed in the tablefor each hypothesis, but the input sample matrices from the consecutivetiming hypotheses change slightly by appending and deleting a column.The updating and the downdating algorithms are potentially applicable tosome of the operations, and the overall computation load may potentiallybe reduced.

Let X(k) represent the sample matrix at time instant k. It may bepartitioned from equation (3) toX (k)=[ x (k),{tilde over (X)}(k+1)],  (30)where{tilde over (X)}(k+1)=[ x (k+1), . . . , x (k+p−1)].  (31)The sample matrix at time k+1 may be expressed asX (k+1)=[{tilde over (X)}(k+1), x (k+p)].  (32)The autocorrelation matrix at time k+1 has the formR _(x)(k+1)=R_(x)(k)− x (k) x ^(T)(k)+x(k+p) x ^(T)(k+p).  (33)This is a combination of a rank-1 downdate and a rank-1 update. Onehyperbolic rotation-based algorithm for updating/downdating the Choleskyfactorization is set forth in Matrix Computations by Golub et al.,3^(rd) edition, 1996, which is hereby incorporated herein in itsentirety by reference.

Another applicable update/downdate algorithm disclosed in Golub et al.text is for QR decomposition, which is based on the Givens rotation. Ofcourse, the given approach that should be used in a particularapplication will depend on factors such as available processingresources, computational complexity, etc., as will be appreciated bythose skilled in the art. Other approaches may also be used, as willalso be appreciated by those skilled in the art.

The performance of the JSTOF based receiver has been evaluated by Matlabsimulations using an extended BLER simulation engine. The parameters forthe JSTOF based receiver can be set with different aspects Examples ofvalues follow:

1) The oversampling ratio (OSR) of 2 can be selected, which maps to thenumber of virtual antennas (M) of 4 in this non-limiting example, andsimulation shows that reducing the OSR to 1 causes significantperformance degradations;

2) A number of temporal delayed samples (N) can be selected as 2.Increasing the number, however, does not always improve the performance;

3) A reduced rank for the channel response matrix can be selected as M.Increasing or decreasing the rank does not necessarily improve theperformance.

4) An auto-switch threshold can be 4.75 dB.

5) A soft decision output can be quantized in 5 bits width. Increasingthe width to 8 bits can improve the performance marginally for DTS-5.Soft decision correction can be enabled.

The AMR speech channel, TCH-AFS12.2 can be used to evaluate theperformance of the JSTOF in terms of FER. The propagation condition TU50km/h-1950 MHz can be assumed throughout the simulations. A simulationran 1000 trials (blocks) for each case.

The FER's of the receiver, against the carrier-to-interference (C/I)ratio, are shown in the graph of FIG. 3. The margins against thereference performance specified are listed in the table below. JSTOFSpec. performance: performance: Margin of Test C/I at FER = C/I at FER =JSTOF against case 1%, dB 1%, dB Spec., dB DTS-1 −2.6 4 6.6 DTS-2 7.3 91.7 DTS-3 7.6 10 2.4 DTS-4 −0.9 6 6.9 DTS-5 7.4 9 1.6The performance of the receiver under pure AWGN and DTS-5 cases with andwithout the auto-switching strategy is shown in the graphs of FIG. 4 andFIG. 5, respectively. The strategy reduced the loss in AWGN by about 1dB (at FER=10%) and incurred little loss for DTS-5.

The JSTOF receiver can include multiple Viterbi equalizers, followed bya multi-channel match filter, which combines the soft decisions afterthe equalizers. A result is shown and compared with the original in thegraph of FIG. 6.

Performance can be evaluated with a modified test case DTS-5R, where thedelay of the asynchronous interferer can be configured. The performanceat 0, ¼, ½ and ¾ of the burst length is shown in the graph of FIG. 7.The results indicate that the performance of JSTOF receiver degrades“slowly” with severe delay of the interferer.

The above-described receiver may advantageously be used in mobilewireless devices (e.g., cellular devices) as well as cellular basestations, for example. An example of a mobile wireless communicationsdevice 1000 that may be used is further described in the example belowwith reference to FIG. 8. The device 1000 illustratively includes ahousing 1200, a keypad 1400 and an output device 1600. The output deviceshown is a display 1600, which is preferably a full graphic LCD. Othertypes of output devices may alternatively be utilized. A processingdevice 1800 is contained within the housing 1200 and is coupled betweenthe keypad 1400 and the display 1600. The processing device 1800controls the operation of the display 1600, as well as the overalloperation of the mobile device 1000, in response to actuation of keys onthe keypad 1400 by the user.

The housing 1200 may be elongated vertically, or may take on other sizesand shapes (including clamshell housing structures). The keypad mayinclude a mode selection key, or other hardware or software forswitching between text entry and telephony entry.

In addition to the processing device 1800, other parts of the mobiledevice 1000 are shown schematically in FIG. 8. These include acommunications subsystem 1001; a short-range communications subsystem1020; the keypad 1400 and the display 1600, along with otherinput/output devices 1060, 1080, 1100 and 1120; as well as memorydevices 1160, 1180 and various other device subsystems 1201. The mobiledevice 1000 is preferably a two-way RE communications device havingvoice and data communications capabilities. In addition, the mobiledevice 1000 preferably has the capability to communicate with othercomputer systems via the Internet.

Operating system software executed by the processing device 1800 ispreferably stored in a persistent store, such as the flash memory 1160,but may be stored in other types of memory devices, such as a read onlymemory (ROM) or similar storage element. In addition, system software,specific device applications, or parts thereof, may be temporarilyloaded into a volatile store, such as the random access memory (RAM)1180. Communications signals received by the mobile device may also bestored in the RAM 1180.

The processing device 1800, in addition to its operating systemfunctions, enables execution of software applications 1300A-1300N on thedevice 1000. A predetermined set of applications that control basicdevice operations, such as data and voice communications 1300A and1300B, may be installed on the device 1000 during manufacture Inaddition, a personal information manager (PIM) application may beinstalled during manufacture. The PIM is preferably capable oforganizing and managing data items, such as e-mail, calendar events,voice mails, appointments, and task items The PIM application is alsopreferably capable of sending and receiving data items via a wirelessnetwork 1401. Preferably, the PIM data items are seamlessly integrated,synchronized and updated via the wireless network 1401 with the deviceuser's corresponding data items stored or associated with a hostcomputer system.

Communication functions, including data and voice communications, areperformed through the communications subsystem 1001, and possiblythrough the short-range communications subsystem. The communicationssubsystem 1001 includes a receiver 1500, a transmitter 1520, and one ormore antennas 1540 and 1560. In addition, the communications subsystem1001 also includes a processing module, such as a digital signalprocessor (DSP) 1580, and local oscillators (LOs) 1601. The specificdesign and implementation of the communications subsystem 1001 isdependent upon the communications network in which the mobile device1000 is intended to operate. For example, a mobile device 1000 mayinclude a communications subsystem 1001 designed to operate with theMobitex™, Data TAC™ or General Packet Radio Service (GPRS) mobile datacommunications networks, and also designed to operate with any of avariety of voice communications networks, such as AMPS, TDMA, CDMA,WCDMA, PCS, GSM, EDGE, etc. Other types of data and voice networks, bothseparate and integrated, may also be utilized with the mobile device1000. The mobile device 1000 may also be compliant with othercommunications standards such as 3GSM, 3GPP, UMTS, etc.

Network access requirements vary depending upon the type ofcommunication system. For example, in the Mobitex and DataTAC networks,mobile devices are registered on the network using a unique personalidentification number or PIN associated with each device. In GPRSnetworks, however, network access is associated with a subscriber oruser of a device. A GPRS device therefore requires a subscriber identitymodule, commonly referred to as a SIN card, in order to operate on aGPRS network.

When required network registration or activation procedures have beencompleted, the mobile device 1000 may send and receive communicationssignals over the communication network 1401. Signals received from thecommunications network 1401 by the antenna 1540 are routed to thereceiver 1500, which provides for signal amplification, frequency downconversion, filtering, channel selection, etc., and may also provideanalog to digital conversion. Analog-to-digital conversion of thereceived signal allows the DSP 1580 to perform more complexcommunications functions, such as demodulation and decoding. In asimilar manner, signals to be transmitted to the network 1401 areprocessed (e.g. modulated and encoded) by the DSP 1580 and are thenprovided to the transmitter 1520 for digital to analog conversion,frequency up conversion, filtering, amplification and transmission tothe communication network 1401 (or networks) via the antenna 1560.

In addition to processing communications signals, the DSP 1580 providesfor control of the receiver 1500 and the transmitter 1520. For example,gains applied to communications signals in the receiver 1500 andtransmitter 1520 may be adaptively controlled through automatic gaincontrol algorithms implemented in the DSP 1580.

In a data communications mode, a received signal, such as a text messageor web page download, is processed by the communications subsystem 1001and is input to the processing device 1800. The received signal is thenfurther processed by the processing device 1800 for an output to thedisplay 1600, or alternatively to some other auxiliary I/O device 1060.A device user may also compose data items, such as e-mail messages,using the keypad 1400 and/or some other auxiliary I/O device 1060, suchas a touchpad, a rocker switch, a thumb-wheel, or some other type ofinput device. The composed data items may then be transmitted over thecommunications network 1401 via the communications subsystem 1001.

In a voice communications mode, overall operation of the device issubstantially similar to the data communications mode, except thatreceived signals are output to a speaker 1100, and signals fortransmission are generated by a microphone 1120. Alternative voice oraudio I/O subsystems, such as a voice message recording subsystem, mayalso be implemented on the device 1000. In addition, the display 1600may also be utilized in voice communications mode, for example todisplay the identity of a calling party, the duration of a voice call,or other voice call related information.

The short-range communications subsystem enables communication betweenthe mobile device 1000 and other proximate systems or devices, whichneed not necessarily be similar devices. For example, the short-rangecommunications subsystem may include an infrared device and associatedcircuits and components, or a Bluetooth™ communications module toprovide for communication with similarly-enabled systems and devices.

Many modifications and other embodiments of the invention will come tothe mind of one skilled in the art having the benefit of the teachingspresented in the foregoing descriptions and the associated drawings.Therefore, it is understood that the invention is not to be limited tothe specific embodiments disclosed, and that modifications andembodiments are intended to be included within the scope of theinvention.

1. A filter for reducing co-channel interference within a communicationsreceiver, the filter comprising: a multi-channel, space-time filtercircuit that filters n signal parts that have been split from acommunications signal by jointly estimating space-time filter weightsand multi-channel impulse responses (CIRs) based upon Cholesky andeigenvalue decompositions; and a multi-channel, matched filter circuitthat receives multi-channel signals from the multi-channel, space-timefilter circuit and has a filter response that is provided by a channelimpulse response estimation from the space-time filter circuit.
 2. Thefilter according to claim 1 further comprising a virtual antenna circuitconnected to said multi-channel, space-time filter circuit that splitsthe communications signal into odd and even sampled, real and imaginaryn signal parts.
 3. The filter according to claim 1 wherein saidmulti-channel, space-time filter circuit comprises at least onemultiplier for multiplying each signal part by a respective space-timefilter weight.
 4. The filter according to claim 3 wherein said at leastone multiplier comprises a pair thereof connected in parallel; andwherein said multi-channel, space-time filter circuit further comprisesa respective delay circuit for each signal part connected to an input ofone of said pair of multipliers
 5. The filter according to claim 4wherein the communications signal comprises a plurality of symbols, andsaid multipliers and delay circuits each having about a one-symbol delayassociated therewith.
 6. The filter according to claim 3 furthercomprising a respective summer circuit, for each channel, for summingoutputs of the multipliers.
 7. The filter according to claim 1 furthercomprising a joint optimal filter weights and channel estimator forreceiving training sequence symbols and timing uncertainty data and forgenerating space-time filter weights for said multi-channel, space-timefilter circuit and a multi-channel impulse response for saidmulti-channel matched filter circuit.
 8. The filter according to claim 1further comprising an equalizer circuit downstream from saidmulti-channel, matched filter circuit.
 9. A filter system for reducingco-channel interference within a communications receiver, the filtersystem comprising: a joint space-time filter comprising a multi-channel,space-time filter circuit that filters n signal parts that have beensplit from a communications signal by jointly estimating space-timefilter weights and multi-channel impulse responses (CIRs) based uponCholesky and eigenvalue decompositions, and a multi-channel, matchedfilter circuit that receives multi-channel signals from themulti-channel, space-time filter circuit and has a filter response thatis provided by a channel impulse response estimation from the space-timefilter circuit; and an alternative filter operative when an interferencelevel is below a predetermined threshold and comprising a matchedfilter, a cross-correlation circuit, and a switch mechanism forswitching the n signal parts into the matched filter andcross-correlation circuit.
 10. The filter system according to claim 9further comprising a virtual antenna circuit connected to saidmulti-channel, space-time filter circuit that splits the communicationssignal into odd and even sampled, real and imaginary n signal parts. 11.The filter system according to claim 9 wherein said multi-channel,space-time filter circuit comprises at least one multiplier formultiplying each signal part by a respective space-time filter weight.12. The filter system according to claim 11 wherein said at least onemultiplier comprises a pair thereof connected in parallel; and whereinsaid multi-channel, space-time filter circuit further comprises arespective delay circuit for each signal part connected to an input ofone of said pair of multipliers.
 13. The filter system according toclaim 12 wherein the communications signal comprises a plurality ofsymbols, and said multipliers and delay circuits each having about aone-symbol delay associated therewith.
 14. The filter system accordingto claim 11 further comprising a respective summer circuit, for eachchannel, for summing outputs of the multipliers.
 15. The filter systemaccording to claim 9 further comprising a joint optimal filter weightsand channel estimator for receiving training sequence symbols and timinguncertainty data and for generating space-time filter weights for saidmulti-channel, space-time filter circuit and a multi-channel impulseresponse for said multi-channel matched filter circuit.
 16. The filtersystem according to claim 9 further comprising an equalizer circuitdownstream from said multi-channel, matched filter circuit
 17. A methodof reducing co-channel interference within a communications receivercomprising: splitting a communications signal into n signal parts,filtering the n signal parts within a multi-channel, space-time filtercircuit and jointly estimating space-time filter weights andmulti-channel channel impulse responses (CIRs) based upon Cholesky andeigenvalue decompositions; and receiving multi-channel signals from thespace-time filter circuit within a multi-channel matched filter circuithaving a filter response that is provided by a channel impulse responseestimation from the space-time filter circuit
 18. The method accordingto claim 17 wherein splitting comprises sampling the communicationssignal into even and odd samples and separating the even and odd samplesinto real and imaginary signal parts.
 19. The method according to claim17 further comprising summing outputs of the matched filter andresealing to a desired level.
 20. The method according to claim 19further comprising equalizing a single channel signal after rescaling toa desired level.
 21. The method according to claim 17 further comprisingfiltering the n-signal parts within an alternative filter when aninterference level is below a threshold.
 22. The method according toclaim 17 further comprising multiplying each signal part based onspace-time filter weights.
 23. The method according to claim 22 furthercomprising summing the signal parts for each channel after multiplying.